阴离子转运蛋白的计算分析揭示了Rv3679和Rv2397c是结核分枝杆菌的潜在药物靶点

Vikas Kumar Jha
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摘要

结核病在全世界仍然是一种严重的、危及生命的疾病。治疗方面最大的挑战之一是耐多药结核病的治疗。耐多药结核病(MDR-TB)的流行对遏制全球结核病流行的常规控制战略的能力构成了威胁。结核病耐药的主要原因是处方不当、不遵守治疗方案、药物供应不稳定以及药物质量欠佳。结核分枝杆菌的细胞壁组成使其对化疗药物的渗透性降低了许多倍,目前的疫苗接种只能提供适度的预防疾病的保护。细胞膜由ATP结合盒家族的蛋白质组成,使结核分枝杆菌能够迅速改变其细胞膜以适应不利的环境。对这种转运蛋白的更深入了解可能会导致更早的发现和更有效的治疗药物靶点。对结核分枝杆菌阴离子转运蛋白进行了全面的计算分析。利用生物信息学工具预测其理化性质、二级结构、功能分析、蛋白-蛋白相互作用、亚细胞定位和分子对接。在所选蛋白中,Rv3679和Rv2397c表现最好,可视为潜在的药物靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational Analysis of Anion Transporter Proteins Revealed Rv3679 and Rv2397c as Potential Drug Targets for Mycobacterium Tuberculosis
Tuberculosis is still a serious, life-threatening disease all throughout the world. One of the greatest therapeutic challenges is the treatment of multidrugresistant tuberculosis. The prevalence of multidrug resistant tuberculosis (MDR-TB) poses a danger to the ability of conventional control strategies to stem the global TB epidemic. Drug resistance in TB is primarily a result of poor prescribing practices, noncompliance with treatment regimens, erratic drug supply, and subpar drug quality. The cell wall composition of Mycobacterium tuberculosis makes it many times less permeable to chemotherapeutic drugs, the current vaccination only provides modest protection against the disease. The cell membrane, which is made up of proteins from the ATP binding cassette family, allows Mycobacterium tuberculosis to quickly alter its cellular membrane to adapt to an unfavourable environment. A greater understanding of such transport proteins may lead to earlier detection and more effective therapeutic drug targets. A thorough computational analyses of Mycobacterium tuberculosis anion transporter proteins was conducted. Bioinformatics tools were used to predict its physiochemical properties, secondary structure, functional analysis, protein-protein interaction, subcellular localization, and molecular docking was also carried out. From the selected proteins, Rv3679 and Rv2397c exhibited best results and can be considered as potential drug targets.
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